Reference library

ML engineering pipelines

Feature prep, batch inference, model-serving hooks, and production ML workflow glue.

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ML engineering pipelines easy

Champion Challenger Deployment Mock in Python

Simulates an A/B champion-challenger ML deployment workflow — comparing two mock model accuracies and deciding which to promote to production.

ml deployment champion-challenger
Python 
import random
import time

class ModelMocker:
    def __init__(self, name="Model", accuracy=0.85):
        self.name = name
        self.accuracy = accuracy

    def predict(self, data):
        """Simulate prediction with some randomness."""
        time.sleep(0.005)  # simulate compute time
        return 1 if rando…
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ML engineering pipelines — Python code examples

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